DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability

TL;DR

This paper introduces DCENWCNet, an ensemble CNN model for white blood cell classification that outperforms existing methods and incorporates LIME for explainability, enhancing interpretability and trust in automated diagnosis.

Contribution

The paper presents a novel ensemble CNN architecture with varied configurations and integrates LIME-based explainability to improve classification accuracy and interpretability.

Findings

Outperforms state-of-the-art models on Rabbin-WBC dataset

Achieves higher accuracy, precision, recall, and F1-score

Provides interpretable explanations for model predictions

Abstract

White blood cells (WBC) are important parts of our immune system, and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. The number of WBC types and the total number of WBCs provide important information about our health status. A traditional method, convolutional neural networks (CNN), a deep learning architecture, can classify the blood cell from a part of an object and perform object recognition. Various CNN models exhibit potential; however, their development often involves ad-hoc processes that neglect unnecessary layers, leading to issues with unbalanced datasets and insufficient data augmentation. To address these challenges, we propose a novel ensemble approach that integrates three CNN architectures, each uniquely configured with different dropout and max-pooling layer settings to enhance feature learning. This ensemble model, named DCENWCNet, effectively balances the bias-variance trade-off. When evaluated on the widely recognized Rabbin-WBC dataset, our model outperforms existing state-of-the-art networks, achieving highest mean accuracy. Additionally, it demonstrates superior performance in precision, recall, F1-score, and Area Under the ROC Curve (AUC) across all categories. To delve deeper into the interpretability of classifiers, we employ reliable post-hoc explanation techniques, including Local Interpretable Model-Agnostic Explanations (LIME). These methods approximate the behavior of a black-box model by elucidating the relationships between feature values and predictions. Interpretable results enable users to comprehend and validate the model's predictions, thereby increasing their confidence in the automated diagnosis.